To investigate the performance difference of the network architectures, we compare the proposed Prior Attention Network with the most popular methods in medical segmentation, including U-Net[1]}, Attention U-Net[2]} and cascaded U-Net, in both 2D and 3D segmentation tasks. It should be noted that, compared with the original version proposed in their own papers, the baseline methods are modified and optimized according to certain tasks in terms of network topology to acquire performance improvement. We introduce residual connections[3]}, batch normalization[4]} and pretrained encoder from ImageNet to the baseline methods for 2D COVID-lesion segmentation task, and we also introduce residual connections, instance normalization and PReLU activations to 3D brain tumor segmentation task. Apart from network topology, the baseline methods share the same data augmentation and training configurations with the proposed Prior Attention Network.
